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News/Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning
arxiv
PublishedJuly 21, 2026 at 4:00 AM

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

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arXiv:2607.16620v1 Announce Type: cross Abstract: Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems. Prior work has shown that DP-SGD can widen accuracy disparities across demographic groups, but this framing treats fairness as a purely

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